// -*- C++ -*-
#ifndef JAMA_EIG_H
#define JAMA_EIG_H

#include "tnt/array1d.h"
#include "tnt/array2d.h"
#include "tnt/math_utils.h"

#include <algorithm>
// for min(), max() below

#include <cmath>
// for abs() below

namespace JAMA {

/**

    Computes eigenvalues and eigenvectors of a real (non-complex)
    matrix.
    <P>
    If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is
    diagonal and the eigenvector matrix V is orthogonal. That is,
    the diagonal values of D are the eigenvalues, and
    V*V' = I, where I is the identity matrix.  The columns of V
    represent the eigenvectors in the sense that A*V = V*D.

    <P>
    If A is not symmetric, then the eigenvalue matrix D is block diagonal
    with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
    a + i*b, in 2-by-2 blocks, [a, b; -b, a].  That is, if the complex
    eigenvalues look like
    <pre>

    u + iv     .        .          .      .    .
    .      u - iv     .          .      .    .
    .        .      a + ib       .      .    .
    .        .        .        a - ib   .    .
    .        .        .          .      x    .
    .        .        .          .      .    y
    </pre>
    then D looks like
    <pre>

    u        v        .          .      .    .
    -v        u        .          .      .    .
    .        .        a          b      .    .
    .        .       -b          a      .    .
    .        .        .          .      x    .
    .        .        .          .      .    y
    </pre>
    This keeps V a real matrix in both symmetric and non-symmetric
    cases, and A*V = V*D.



    <p>
    The matrix V may be badly
    conditioned, or even singular, so the validity of the equation
    A = V*D*inverse(V) depends upon the condition number of V.

    <p>
    (Adapted from JAMA, a Java Matrix Library, developed by jointly
    by the Mathworks and NIST; see  http://math.nist.gov/javanumerics/jama).
**/

template <class Real> class Eigenvalue {

  /** Row and column dimension (square matrix).  */
  int n;

  int issymmetric; /* boolean*/

  /** TNT::Arrays for internal storage of eigenvalues. */

  TNT::TNT::Array1D<Real> d; /* real part */
  TNT::TNT::Array1D<Real> e; /* img part */

  /** TNT::Array for internal storage of eigenvectors. */
  TNT::TNT::Array2D<Real> V;

  /** TNT::Array for internal storage of nonsymmetric Hessenberg form.
      @serial internal storage of nonsymmetric Hessenberg form.
  */
  TNT::TNT::Array2D<Real> H;

  /** Working storage for nonsymmetric algorithm.
      @serial working storage for nonsymmetric algorithm.
  */
  TNT::TNT::Array1D<Real> ort;

  // Symmetric Householder reduction to tridiagonal form.

  void tred2() {

    //  This is derived from the Algol procedures tred2 by
    //  Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
    //  Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
    //  Fortran subroutine in EISPACK.

    for (int j = 0; j < n; j++) {
      d[j] = V[n - 1][j];
    }

    // Householder reduction to tridiagonal form.

    for (int i = n - 1; i > 0; i--) {

      // Scale to avoid under/overflow.

      Real scale = 0.0;
      Real h = 0.0;
      for (int k = 0; k < i; k++) {
        scale = scale + std::abs(d[k]);
      }
      if (scale == 0.0) {
        e[i] = d[i - 1];
        for (int j = 0; j < i; j++) {
          d[j] = V[i - 1][j];
          V[i][j] = 0.0;
          V[j][i] = 0.0;
        }
      } else {

        // Generate Householder vector.

        for (int k = 0; k < i; k++) {
          d[k] /= scale;
          h += d[k] * d[k];
        }
        Real f = d[i - 1];
        Real g = std::sqrt(h);
        if (f > 0) {
          g = -g;
        }
        e[i] = scale * g;
        h = h - f * g;
        d[i - 1] = f - g;
        for (int j = 0; j < i; j++) {
          e[j] = 0.0;
        }

        // Apply similarity transformation to remaining columns.

        for (int j = 0; j < i; j++) {
          f = d[j];
          V[j][i] = f;
          g = e[j] + V[j][j] * f;
          for (int k = j + 1; k <= i - 1; k++) {
            g += V[k][j] * d[k];
            e[k] += V[k][j] * f;
          }
          e[j] = g;
        }
        f = 0.0;
        for (int j = 0; j < i; j++) {
          e[j] /= h;
          f += e[j] * d[j];
        }
        Real hh = f / (h + h);
        for (int j = 0; j < i; j++) {
          e[j] -= hh * d[j];
        }
        for (int j = 0; j < i; j++) {
          f = d[j];
          g = e[j];
          for (int k = j; k <= i - 1; k++) {
            V[k][j] -= (f * e[k] + g * d[k]);
          }
          d[j] = V[i - 1][j];
          V[i][j] = 0.0;
        }
      }
      d[i] = h;
    }

    // Accumulate transformations.

    for (int i = 0; i < n - 1; i++) {
      V[n - 1][i] = V[i][i];
      V[i][i] = 1.0;
      Real h = d[i + 1];
      if (h != 0.0) {
        for (int k = 0; k <= i; k++) {
          d[k] = V[k][i + 1] / h;
        }
        for (int j = 0; j <= i; j++) {
          Real g = 0.0;
          for (int k = 0; k <= i; k++) {
            g += V[k][i + 1] * V[k][j];
          }
          for (int k = 0; k <= i; k++) {
            V[k][j] -= g * d[k];
          }
        }
      }
      for (int k = 0; k <= i; k++) {
        V[k][i + 1] = 0.0;
      }
    }
    for (int j = 0; j < n; j++) {
      d[j] = V[n - 1][j];
      V[n - 1][j] = 0.0;
    }
    V[n - 1][n - 1] = 1.0;
    e[0] = 0.0;
  }

  // Symmetric tridiagonal QL algorithm.

  void tql2() {

    //  This is derived from the Algol procedures tql2, by
    //  Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
    //  Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
    //  Fortran subroutine in EISPACK.

    for (int i = 1; i < n; i++) {
      e[i - 1] = e[i];
    }
    e[n - 1] = 0.0;

    Real f = 0.0;
    Real tst1 = 0.0;
    Real eps = pow(2.0, -52.0);
    for (int l = 0; l < n; l++) {

      // Find small subdiagonal element

      tst1 = std::max(tst1, std::abs(d[l]) + std::abs(e[l]));
      int m = l;

      // Original while-loop from Java code
      while (m < n) {
        if (std::abs(e[m]) <= eps * tst1) {
          break;
        }
        m++;
      }

      // If m == l, d[l] is an eigenvalue,
      // otherwise, iterate.

      if (m > l) {
        int iter = 0;
        do {
          iter = iter + 1; // (Could check iteration count here.)

          // Compute implicit shift

          Real g = d[l];
          Real p = (d[l + 1] - g) / (2.0 * e[l]);
          Real r = hypot(p, 1.0);
          if (p < 0) {
            r = -r;
          }
          d[l] = e[l] / (p + r);
          d[l + 1] = e[l] * (p + r);
          Real dl1 = d[l + 1];
          Real h = g - d[l];
          for (int i = l + 2; i < n; i++) {
            d[i] -= h;
          }
          f = f + h;

          // Implicit QL transformation.

          p = d[m];
          Real c = 1.0;
          Real c2 = c;
          Real c3 = c;
          Real el1 = e[l + 1];
          Real s = 0.0;
          Real s2 = 0.0;
          for (int i = m - 1; i >= l; i--) {
            c3 = c2;
            c2 = c;
            s2 = s;
            g = c * e[i];
            h = c * p;
            r = hypot(p, e[i]);
            e[i + 1] = s * r;
            s = e[i] / r;
            c = p / r;
            p = c * d[i] - s * g;
            d[i + 1] = h + s * (c * g + s * d[i]);

            // Accumulate transformation.

            for (int k = 0; k < n; k++) {
              h = V[k][i + 1];
              V[k][i + 1] = s * V[k][i] + c * h;
              V[k][i] = c * V[k][i] - s * h;
            }
          }
          p = -s * s2 * c3 * el1 * e[l] / dl1;
          e[l] = s * p;
          d[l] = c * p;

          // Check for convergence.

        } while (std::abs(e[l]) > eps * tst1);
      }
      d[l] = d[l] + f;
      e[l] = 0.0;
    }

    // Sort eigenvalues and corresponding vectors.

    for (int i = 0; i < n - 1; i++) {
      int k = i;
      Real p = d[i];
      for (int j = i + 1; j < n; j++) {
        if (d[j] < p) {
          k = j;
          p = d[j];
        }
      }
      if (k != i) {
        d[k] = d[i];
        d[i] = p;
        for (int j = 0; j < n; j++) {
          p = V[j][i];
          V[j][i] = V[j][k];
          V[j][k] = p;
        }
      }
    }
  }

  // Nonsymmetric reduction to Hessenberg form.

  void orthes() {

    //  This is derived from the Algol procedures orthes and ortran,
    //  by Martin and Wilkinson, Handbook for Auto. Comp.,
    //  Vol.ii-Linear Algebra, and the corresponding
    //  Fortran subroutines in EISPACK.

    int low = 0;
    int high = n - 1;

    for (int m = low + 1; m <= high - 1; m++) {

      // Scale column.

      Real scale = 0.0;
      for (int i = m; i <= high; i++) {
        scale = scale + std::abs(H[i][m - 1]);
      }
      if (scale != 0.0) {

        // Compute Householder transformation.

        Real h = 0.0;
        for (int i = high; i >= m; i--) {
          ort[i] = H[i][m - 1] / scale;
          h += ort[i] * ort[i];
        }
        Real g = std::sqrt(h);
        if (ort[m] > 0) {
          g = -g;
        }
        h = h - ort[m] * g;
        ort[m] = ort[m] - g;

        // Apply Householder similarity transformation
        // H = (I-u*u'/h)*H*(I-u*u')/h)

        for (int j = m; j < n; j++) {
          Real f = 0.0;
          for (int i = high; i >= m; i--) {
            f += ort[i] * H[i][j];
          }
          f = f / h;
          for (int i = m; i <= high; i++) {
            H[i][j] -= f * ort[i];
          }
        }

        for (int i = 0; i <= high; i++) {
          Real f = 0.0;
          for (int j = high; j >= m; j--) {
            f += ort[j] * H[i][j];
          }
          f = f / h;
          for (int j = m; j <= high; j++) {
            H[i][j] -= f * ort[j];
          }
        }
        ort[m] = scale * ort[m];
        H[m][m - 1] = scale * g;
      }
    }

    // Accumulate transformations (Algol's ortran).

    for (int i = 0; i < n; i++) {
      for (int j = 0; j < n; j++) {
        V[i][j] = (i == j ? 1.0 : 0.0);
      }
    }

    for (int m = high - 1; m >= low + 1; m--) {
      if (H[m][m - 1] != 0.0) {
        for (int i = m + 1; i <= high; i++) {
          ort[i] = H[i][m - 1];
        }
        for (int j = m; j <= high; j++) {
          Real g = 0.0;
          for (int i = m; i <= high; i++) {
            g += ort[i] * V[i][j];
          }
          // Double division avoids possible underflow
          g = (g / ort[m]) / H[m][m - 1];
          for (int i = m; i <= high; i++) {
            V[i][j] += g * ort[i];
          }
        }
      }
    }
  }

  // Complex scalar division.

  Real cdivr, cdivi;
  void cdiv(Real xr, Real xi, Real yr, Real yi) {
    Real r, d;
    if (std::abs(yr) > std::abs(yi)) {
      r = yi / yr;
      d = yr + r * yi;
      cdivr = (xr + r * xi) / d;
      cdivi = (xi - r * xr) / d;
    } else {
      r = yr / yi;
      d = yi + r * yr;
      cdivr = (r * xr + xi) / d;
      cdivi = (r * xi - xr) / d;
    }
  }

  // Nonsymmetric reduction from Hessenberg to real Schur form.

  void hqr2() {

    //  This is derived from the Algol procedure hqr2,
    //  by Martin and Wilkinson, Handbook for Auto. Comp.,
    //  Vol.ii-Linear Algebra, and the corresponding
    //  Fortran subroutine in EISPACK.

    // Initialize

    int nn = this->n;
    int n = nn - 1;
    int low = 0;
    int high = nn - 1;
    Real eps = pow(2.0, -52.0);
    Real exshift = 0.0;
    Real p = 0, q = 0, r = 0, s = 0, z = 0, t, w, x, y;

    // Store roots isolated by balanc and compute matrix norm

    Real norm = 0.0;
    for (int i = 0; i < nn; i++) {
      if ((i < low) || (i > high)) {
        d[i] = H[i][i];
        e[i] = 0.0;
      }
      for (int j = std::max(i - 1, 0); j < nn; j++) {
        norm = norm + std::abs(H[i][j]);
      }
    }

    // Outer loop over eigenvalue index

    int iter = 0;
    while (n >= low) {

      // Look for single small sub-diagonal element

      int l = n;
      while (l > low) {
        s = std::abs(H[l - 1][l - 1]) + std::abs(H[l][l]);
        if (s == 0.0) {
          s = norm;
        }
        if (std::abs(H[l][l - 1]) < eps * s) {
          break;
        }
        l--;
      }

      // Check for convergence
      // One root found

      if (l == n) {
        H[n][n] = H[n][n] + exshift;
        d[n] = H[n][n];
        e[n] = 0.0;
        n--;
        iter = 0;

        // Two roots found

      } else if (l == n - 1) {
        w = H[n][n - 1] * H[n - 1][n];
        p = (H[n - 1][n - 1] - H[n][n]) / 2.0;
        q = p * p + w;
        z = std::sqrt(std::abs(q));
        H[n][n] = H[n][n] + exshift;
        H[n - 1][n - 1] = H[n - 1][n - 1] + exshift;
        x = H[n][n];

        // Real pair

        if (q >= 0) {
          if (p >= 0) {
            z = p + z;
          } else {
            z = p - z;
          }
          d[n - 1] = x + z;
          d[n] = d[n - 1];
          if (z != 0.0) {
            d[n] = x - w / z;
          }
          e[n - 1] = 0.0;
          e[n] = 0.0;
          x = H[n][n - 1];
          s = std::abs(x) + std::abs(z);
          p = x / s;
          q = z / s;
          r = std::sqrt(p * p + q * q);
          p = p / r;
          q = q / r;

          // Row modification

          for (int j = n - 1; j < nn; j++) {
            z = H[n - 1][j];
            H[n - 1][j] = q * z + p * H[n][j];
            H[n][j] = q * H[n][j] - p * z;
          }

          // Column modification

          for (int i = 0; i <= n; i++) {
            z = H[i][n - 1];
            H[i][n - 1] = q * z + p * H[i][n];
            H[i][n] = q * H[i][n] - p * z;
          }

          // Accumulate transformations

          for (int i = low; i <= high; i++) {
            z = V[i][n - 1];
            V[i][n - 1] = q * z + p * V[i][n];
            V[i][n] = q * V[i][n] - p * z;
          }

          // Complex pair

        } else {
          d[n - 1] = x + p;
          d[n] = x + p;
          e[n - 1] = z;
          e[n] = -z;
        }
        n = n - 2;
        iter = 0;

        // No convergence yet

      } else {

        // Form shift

        x = H[n][n];
        y = 0.0;
        w = 0.0;
        if (l < n) {
          y = H[n - 1][n - 1];
          w = H[n][n - 1] * H[n - 1][n];
        }

        // Wilkinson's original ad hoc shift

        if (iter == 10) {
          exshift += x;
          for (int i = low; i <= n; i++) {
            H[i][i] -= x;
          }
          s = std::abs(H[n][n - 1]) + std::abs(H[n - 1][n - 2]);
          x = y = 0.75 * s;
          w = -0.4375 * s * s;
        }

        // MATLAB's new ad hoc shift

        if (iter == 30) {
          s = (y - x) / 2.0;
          s = s * s + w;
          if (s > 0) {
            s = std::sqrt(s);
            if (y < x) {
              s = -s;
            }
            s = x - w / ((y - x) / 2.0 + s);
            for (int i = low; i <= n; i++) {
              H[i][i] -= s;
            }
            exshift += s;
            x = y = w = 0.964;
          }
        }

        iter = iter + 1; // (Could check iteration count here.)

        // Look for two consecutive small sub-diagonal elements

        int m = n - 2;
        while (m >= l) {
          z = H[m][m];
          r = x - z;
          s = y - z;
          p = (r * s - w) / H[m + 1][m] + H[m][m + 1];
          q = H[m + 1][m + 1] - z - r - s;
          r = H[m + 2][m + 1];
          s = std::abs(p) + std::abs(q) + std::abs(r);
          p = p / s;
          q = q / s;
          r = r / s;
          if (m == l) {
            break;
          }
          if (std::abs(H[m][m - 1]) * (std::abs(q) + std::abs(r)) <
              eps * (std::abs(p) * (std::abs(H[m - 1][m - 1]) + std::abs(z) +
                                    std::abs(H[m + 1][m + 1])))) {
            break;
          }
          m--;
        }

        for (int i = m + 2; i <= n; i++) {
          H[i][i - 2] = 0.0;
          if (i > m + 2) {
            H[i][i - 3] = 0.0;
          }
        }

        // Double QR step involving rows l:n and columns m:n

        for (int k = m; k <= n - 1; k++) {
          int notlast = (k != n - 1);
          if (k != m) {
            p = H[k][k - 1];
            q = H[k + 1][k - 1];
            r = (notlast ? H[k + 2][k - 1] : 0.0);
            x = std::abs(p) + std::abs(q) + std::abs(r);
            if (x != 0.0) {
              p = p / x;
              q = q / x;
              r = r / x;
            }
          }
          if (x == 0.0) {
            break;
          }
          s = std::sqrt(p * p + q * q + r * r);
          if (p < 0) {
            s = -s;
          }
          if (s != 0) {
            if (k != m) {
              H[k][k - 1] = -s * x;
            } else if (l != m) {
              H[k][k - 1] = -H[k][k - 1];
            }
            p = p + s;
            x = p / s;
            y = q / s;
            z = r / s;
            q = q / p;
            r = r / p;

            // Row modification

            for (int j = k; j < nn; j++) {
              p = H[k][j] + q * H[k + 1][j];
              if (notlast) {
                p = p + r * H[k + 2][j];
                H[k + 2][j] = H[k + 2][j] - p * z;
              }
              H[k][j] = H[k][j] - p * x;
              H[k + 1][j] = H[k + 1][j] - p * y;
            }

            // Column modification

            for (int i = 0; i <= std::min(n, k + 3); i++) {
              p = x * H[i][k] + y * H[i][k + 1];
              if (notlast) {
                p = p + z * H[i][k + 2];
                H[i][k + 2] = H[i][k + 2] - p * r;
              }
              H[i][k] = H[i][k] - p;
              H[i][k + 1] = H[i][k + 1] - p * q;
            }

            // Accumulate transformations

            for (int i = low; i <= high; i++) {
              p = x * V[i][k] + y * V[i][k + 1];
              if (notlast) {
                p = p + z * V[i][k + 2];
                V[i][k + 2] = V[i][k + 2] - p * r;
              }
              V[i][k] = V[i][k] - p;
              V[i][k + 1] = V[i][k + 1] - p * q;
            }
          } // (s != 0)
        }   // k loop
      }     // check convergence
    }       // while (n >= low)

    // Backsubstitute to find vectors of upper triangular form

    if (norm == 0.0) {
      return;
    }

    for (n = nn - 1; n >= 0; n--) {
      p = d[n];
      q = e[n];

      // Real vector

      if (q == 0) {
        int l = n;
        H[n][n] = 1.0;
        for (int i = n - 1; i >= 0; i--) {
          w = H[i][i] - p;
          r = 0.0;
          for (int j = l; j <= n; j++) {
            r = r + H[i][j] * H[j][n];
          }
          if (e[i] < 0.0) {
            z = w;
            s = r;
          } else {
            l = i;
            if (e[i] == 0.0) {
              if (w != 0.0) {
                H[i][n] = -r / w;
              } else {
                H[i][n] = -r / (eps * norm);
              }

              // Solve real equations

            } else {
              x = H[i][i + 1];
              y = H[i + 1][i];
              q = (d[i] - p) * (d[i] - p) + e[i] * e[i];
              t = (x * s - z * r) / q;
              H[i][n] = t;
              if (std::abs(x) > std::abs(z)) {
                H[i + 1][n] = (-r - w * t) / x;
              } else {
                H[i + 1][n] = (-s - y * t) / z;
              }
            }

            // Overflow control

            t = std::abs(H[i][n]);
            if ((eps * t) * t > 1) {
              for (int j = i; j <= n; j++) {
                H[j][n] = H[j][n] / t;
              }
            }
          }
        }

        // Complex vector

      } else if (q < 0) {
        int l = n - 1;

        // Last vector component imaginary so matrix is triangular

        if (std::abs(H[n][n - 1]) > std::abs(H[n - 1][n])) {
          H[n - 1][n - 1] = q / H[n][n - 1];
          H[n - 1][n] = -(H[n][n] - p) / H[n][n - 1];
        } else {
          cdiv(0.0, -H[n - 1][n], H[n - 1][n - 1] - p, q);
          H[n - 1][n - 1] = cdivr;
          H[n - 1][n] = cdivi;
        }
        H[n][n - 1] = 0.0;
        H[n][n] = 1.0;
        for (int i = n - 2; i >= 0; i--) {
          Real ra, sa, vr, vi;
          ra = 0.0;
          sa = 0.0;
          for (int j = l; j <= n; j++) {
            ra = ra + H[i][j] * H[j][n - 1];
            sa = sa + H[i][j] * H[j][n];
          }
          w = H[i][i] - p;

          if (e[i] < 0.0) {
            z = w;
            r = ra;
            s = sa;
          } else {
            l = i;
            if (e[i] == 0) {
              cdiv(-ra, -sa, w, q);
              H[i][n - 1] = cdivr;
              H[i][n] = cdivi;
            } else {

              // Solve complex equations

              x = H[i][i + 1];
              y = H[i + 1][i];
              vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q;
              vi = (d[i] - p) * 2.0 * q;
              if ((vr == 0.0) && (vi == 0.0)) {
                vr = eps * norm * (std::abs(w) + std::abs(q) + std::abs(x) +
                                   std::abs(y) + std::abs(z));
              }
              cdiv(x * r - z * ra + q * sa, x * s - z * sa - q * ra, vr, vi);
              H[i][n - 1] = cdivr;
              H[i][n] = cdivi;
              if (std::abs(x) > (std::abs(z) + std::abs(q))) {
                H[i + 1][n - 1] = (-ra - w * H[i][n - 1] + q * H[i][n]) / x;
                H[i + 1][n] = (-sa - w * H[i][n] - q * H[i][n - 1]) / x;
              } else {
                cdiv(-r - y * H[i][n - 1], -s - y * H[i][n], z, q);
                H[i + 1][n - 1] = cdivr;
                H[i + 1][n] = cdivi;
              }
            }

            // Overflow control

            t = std::max(std::abs(H[i][n - 1]), std::abs(H[i][n]));
            if ((eps * t) * t > 1) {
              for (int j = i; j <= n; j++) {
                H[j][n - 1] = H[j][n - 1] / t;
                H[j][n] = H[j][n] / t;
              }
            }
          }
        }
      }
    }

    // Vectors of isolated roots

    for (int i = 0; i < nn; i++) {
      if (i < low || i > high) {
        for (int j = i; j < nn; j++) {
          V[i][j] = H[i][j];
        }
      }
    }

    // Back transformation to get eigenvectors of original matrix

    for (int j = nn - 1; j >= low; j--) {
      for (int i = low; i <= high; i++) {
        z = 0.0;
        for (int k = low; k <= std::min(j, high); k++) {
          z = z + V[i][k] * H[k][j];
        }
        V[i][j] = z;
      }
    }
  }

public:
  /** Check for symmetry, then construct the eigenvalue decomposition
      @param A    Square real (non-complex) matrix
  */

  Eigenvalue(const TNT::TNT::Array2D<Real> &A) {
    n = A.dim2();
    V = TNT::Array2D<Real>(n, n);
    d = TNT::Array1D<Real>(n);
    e = TNT::Array1D<Real>(n);

    issymmetric = 1;
    for (int j = 0; (j < n) && issymmetric; j++) {
      for (int i = 0; (i < n) && issymmetric; i++) {
        issymmetric = (A[i][j] == A[j][i]);
      }
    }

    if (issymmetric) {
      for (int i = 0; i < n; i++) {
        for (int j = 0; j < n; j++) {
          V[i][j] = A[i][j];
        }
      }

      // Tridiagonalize.
      tred2();

      // Diagonalize.
      tql2();

    } else {
      H = TNT::TNT::Array2D<Real>(n, n);
      ort = TNT::TNT::Array1D<Real>(n);

      for (int j = 0; j < n; j++) {
        for (int i = 0; i < n; i++) {
          H[i][j] = A[i][j];
        }
      }

      // Reduce to Hessenberg form.
      orthes();

      // Reduce Hessenberg to real Schur form.
      hqr2();
    }
  }

  /** Return the eigenvector matrix
      @return     V
  */

  void getV(TNT::TNT::Array2D<Real> &V_) {
    V_ = V;
    return;
  }

  /** Return the real parts of the eigenvalues
      @return     real(diag(D))
  */

  void getRealEigenvalues(TNT::TNT::Array1D<Real> &d_) {
    d_ = d;
    return;
  }

  /** Return the imaginary parts of the eigenvalues
      in parameter e_.

      @pararm e_: new matrix with imaginary parts of the eigenvalues.
  */
  void getImagEigenvalues(TNT::TNT::Array1D<Real> &e_) {
    e_ = e;
    return;
  }

  /**
      Computes the block diagonal eigenvalue matrix.
      If the original matrix A is not symmetric, then the eigenvalue
      matrix D is block diagonal with the real eigenvalues in 1-by-1
      blocks and any complex eigenvalues,
      a + i*b, in 2-by-2 blocks, [a, b; -b, a].  That is, if the complex
      eigenvalues look like
      <pre>

      u + iv     .        .          .      .    .
      .      u - iv     .          .      .    .
      .        .      a + ib       .      .    .
      .        .        .        a - ib   .    .
      .        .        .          .      x    .
      .        .        .          .      .    y
      </pre>
      then D looks like
      <pre>

      u        v        .          .      .    .
      -v        u        .          .      .    .
      .        .        a          b      .    .
      .        .       -b          a      .    .
      .        .        .          .      x    .
      .        .        .          .      .    y
      </pre>
      This keeps V a real matrix in both symmetric and non-symmetric
      cases, and A*V = V*D.

      @param D: upon return, the matrix is filled with the block diagonal
      eigenvalue matrix.

  */
  void getD(TNT::TNT::Array2D<Real> &D) {
    D = TNT::Array2D<Real>(n, n);
    for (int i = 0; i < n; i++) {
      for (int j = 0; j < n; j++) {
        D[i][j] = 0.0;
      }
      D[i][i] = d[i];
      if (e[i] > 0) {
        D[i][i + 1] = e[i];
      } else if (e[i] < 0) {
        D[i][i - 1] = e[i];
      }
    }
  }
};

} // namespace JAMA

#endif
// JAMA_EIG_H
